Overview

Dataset statistics

Number of variables18
Number of observations177512
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.2 MiB
Average record size in memory137.0 B

Variable types

Numeric9
Categorical8
Boolean1

Warnings

rate is highly correlated with rate_varHigh correlation
rate_var is highly correlated with rateHigh correlation
provider_name is highly correlated with click_typeHigh correlation
click_type is highly correlated with provider_nameHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Time_of_day has 2817 (1.6%) zeros Zeros
rate_var has 20961 (11.8%) zeros Zeros
Big4Weight has 83856 (47.2%) zeros Zeros

Reproduction

Analysis started2021-02-01 02:39:02.428265
Analysis finished2021-02-01 02:40:14.204074
Duration1 minute and 11.78 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct177512
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88756.5
Minimum1
Maximum177512
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2021-02-01T02:40:14.493951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8876.55
Q144378.75
median88756.5
Q3133134.25
95-th percentile168636.45
Maximum177512
Range177511
Interquartile range (IQR)88755.5

Descriptive statistics

Standard deviation51243.44483
Coefficient of variation (CV)0.577348643
Kurtosis-1.2
Mean88756.5
Median Absolute Deviation (MAD)44378
Skewness0
Sum1.575534383 × 1010
Variance2625890638
MonotocityStrictly increasing
2021-02-01T02:40:14.704574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
648211
 
< 0.1%
484451
 
< 0.1%
463961
 
< 0.1%
361551
 
< 0.1%
341061
 
< 0.1%
402491
 
< 0.1%
382001
 
< 0.1%
607271
 
< 0.1%
586781
 
< 0.1%
Other values (177502)177502
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
ValueCountFrequency (%)
1775121
< 0.1%
1775111
< 0.1%
1775101
< 0.1%
1775091
< 0.1%
1775081
< 0.1%

click_type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
GTS
153563 
Lead
23949 

Length

Max length4
Median length3
Mean length3.134914823
Min length3

Characters and Unicode

Total characters556485
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGTS
2nd rowGTS
3rd rowGTS
4th rowGTS
5th rowGTS
ValueCountFrequency (%)
GTS153563
86.5%
Lead23949
 
13.5%
2021-02-01T02:40:15.111025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-01T02:40:15.235251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
gts153563
86.5%
lead23949
 
13.5%

Most occurring characters

ValueCountFrequency (%)
G153563
27.6%
T153563
27.6%
S153563
27.6%
L23949
 
4.3%
e23949
 
4.3%
a23949
 
4.3%
d23949
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter484638
87.1%
Lowercase Letter71847
 
12.9%

Most frequent character per category

ValueCountFrequency (%)
G153563
31.7%
T153563
31.7%
S153563
31.7%
L23949
 
4.9%
ValueCountFrequency (%)
e23949
33.3%
a23949
33.3%
d23949
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin556485
100.0%

Most frequent character per script

ValueCountFrequency (%)
G153563
27.6%
T153563
27.6%
S153563
27.6%
L23949
 
4.3%
e23949
 
4.3%
a23949
 
4.3%
d23949
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII556485
100.0%

Most frequent character per block

ValueCountFrequency (%)
G153563
27.6%
T153563
27.6%
S153563
27.6%
L23949
 
4.3%
e23949
 
4.3%
a23949
 
4.3%
d23949
 
4.3%

Time_of_day
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.18273694
Minimum0
Maximum23
Zeros2817
Zeros (%)1.6%
Memory size1.4 MiB
2021-02-01T02:40:15.343987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median14
Q319
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.365422795
Coefficient of variation (CV)0.3783065862
Kurtosis-0.4120961924
Mean14.18273694
Median Absolute Deviation (MAD)4
Skewness-0.360679859
Sum2517606
Variance28.78776177
MonotocityNot monotonic
2021-02-01T02:40:15.492585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2011606
 
6.5%
1311429
 
6.4%
1211354
 
6.4%
1411221
 
6.3%
2111131
 
6.3%
1610975
 
6.2%
1510848
 
6.1%
1010687
 
6.0%
1110337
 
5.8%
1910280
 
5.8%
Other values (14)67644
38.1%
ValueCountFrequency (%)
02817
1.6%
11594
0.9%
2967
 
0.5%
3686
 
0.4%
41013
 
0.6%
ValueCountFrequency (%)
235349
3.0%
228422
4.7%
2111131
6.3%
2011606
6.5%
1910280
5.8%

weekday
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Wednesday
31441 
Tuesday
30559 
Monday
27672 
Thursday
27468 
Friday
24726 
Other values (2)
35646 

Length

Max length9
Median length7
Mean length7.221247014
Min length6

Characters and Unicode

Total characters1281858
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowFriday
3rd rowFriday
4th rowFriday
5th rowFriday
ValueCountFrequency (%)
Wednesday31441
17.7%
Tuesday30559
17.2%
Monday27672
15.6%
Thursday27468
15.5%
Friday24726
13.9%
Saturday18484
10.4%
Sunday17162
9.7%
2021-02-01T02:40:15.856514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-01T02:40:15.974740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
wednesday31441
17.7%
tuesday30559
17.2%
monday27672
15.6%
thursday27468
15.5%
friday24726
13.9%
saturday18484
10.4%
sunday17162
9.7%

Most occurring characters

ValueCountFrequency (%)
d208953
16.3%
a195996
15.3%
y177512
13.8%
u93673
7.3%
e93441
7.3%
s89468
7.0%
n76275
 
6.0%
r70678
 
5.5%
T58027
 
4.5%
S35646
 
2.8%
Other values (7)182189
14.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1104346
86.2%
Uppercase Letter177512
 
13.8%

Most frequent character per category

ValueCountFrequency (%)
d208953
18.9%
a195996
17.7%
y177512
16.1%
u93673
8.5%
e93441
8.5%
s89468
8.1%
n76275
 
6.9%
r70678
 
6.4%
o27672
 
2.5%
h27468
 
2.5%
Other values (2)43210
 
3.9%
ValueCountFrequency (%)
T58027
32.7%
S35646
20.1%
W31441
17.7%
M27672
15.6%
F24726
13.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1281858
100.0%

Most frequent character per script

ValueCountFrequency (%)
d208953
16.3%
a195996
15.3%
y177512
13.8%
u93673
7.3%
e93441
7.3%
s89468
7.0%
n76275
 
6.0%
r70678
 
5.5%
T58027
 
4.5%
S35646
 
2.8%
Other values (7)182189
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1281858
100.0%

Most frequent character per block

ValueCountFrequency (%)
d208953
16.3%
a195996
15.3%
y177512
13.8%
u93673
7.3%
e93441
7.3%
s89468
7.0%
n76275
 
6.0%
r70678
 
5.5%
T58027
 
4.5%
S35646
 
2.8%
Other values (7)182189
14.2%

position
Real number (ℝ≥0)

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.148046329
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2021-02-01T02:40:16.148061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q313
95-th percentile18
Maximum23
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.537035408
Coefficient of variation (CV)0.6795537462
Kurtosis-0.8320594955
Mean8.148046329
Median Absolute Deviation (MAD)4
Skewness0.5020465854
Sum1446376
Variance30.6587611
MonotocityNot monotonic
2021-02-01T02:40:16.326036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
117073
 
9.6%
215733
 
8.9%
314139
 
8.0%
412829
 
7.2%
511951
 
6.7%
611332
 
6.4%
710756
 
6.1%
810179
 
5.7%
99066
 
5.1%
128788
 
5.0%
Other values (13)55666
31.4%
ValueCountFrequency (%)
117073
9.6%
215733
8.9%
314139
8.0%
412829
7.2%
511951
6.7%
ValueCountFrequency (%)
23254
 
0.1%
22415
 
0.2%
212139
1.2%
201753
1.0%
193251
1.8%

device
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
desktop
87534 
mobile
76965 
tablet
13013 

Length

Max length7
Median length6
Mean length6.493115958
Min length6

Characters and Unicode

Total characters1152606
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmobile
2nd rowmobile
3rd rowmobile
4th rowmobile
5th rowmobile
ValueCountFrequency (%)
desktop87534
49.3%
mobile76965
43.4%
tablet13013
 
7.3%
2021-02-01T02:40:16.680612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-01T02:40:16.786152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
desktop87534
49.3%
mobile76965
43.4%
tablet13013
 
7.3%

Most occurring characters

ValueCountFrequency (%)
e177512
15.4%
o164499
14.3%
t113560
9.9%
b89978
7.8%
l89978
7.8%
d87534
7.6%
s87534
7.6%
k87534
7.6%
p87534
7.6%
m76965
6.7%
Other values (2)89978
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1152606
100.0%

Most frequent character per category

ValueCountFrequency (%)
e177512
15.4%
o164499
14.3%
t113560
9.9%
b89978
7.8%
l89978
7.8%
d87534
7.6%
s87534
7.6%
k87534
7.6%
p87534
7.6%
m76965
6.7%
Other values (2)89978
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin1152606
100.0%

Most frequent character per script

ValueCountFrequency (%)
e177512
15.4%
o164499
14.3%
t113560
9.9%
b89978
7.8%
l89978
7.8%
d87534
7.6%
s87534
7.6%
k87534
7.6%
p87534
7.6%
m76965
6.7%
Other values (2)89978
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1152606
100.0%

Most frequent character per block

ValueCountFrequency (%)
e177512
15.4%
o164499
14.3%
t113560
9.9%
b89978
7.8%
l89978
7.8%
d87534
7.6%
s87534
7.6%
k87534
7.6%
p87534
7.6%
m76965
6.7%
Other values (2)89978
7.8%

provider_name
Categorical

HIGH CORRELATION

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
loans.com.au
27660 
UBank
14305 
HSBC
13945 
ME
12975 
Virgin Money
11653 
Other values (24)
96974 

Length

Max length19
Median length9
Mean length9.367428681
Min length2

Characters and Unicode

Total characters1662831
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowNewcastle Permanent
2nd rowME
3rd rowNAB
4th rowAMP
5th rowloans.com.au
ValueCountFrequency (%)
loans.com.au27660
15.6%
UBank14305
 
8.1%
HSBC13945
 
7.9%
ME12975
 
7.3%
Virgin Money11653
 
6.6%
Newcastle Permanent9628
 
5.4%
Athena9242
 
5.2%
Hunter United8721
 
4.9%
Bank Australia6909
 
3.9%
QBANK6467
 
3.6%
Other values (19)56007
31.6%
2021-02-01T02:40:17.095764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loans.com.au27660
 
11.0%
bank26553
 
10.6%
ubank14305
 
5.7%
hsbc13945
 
5.6%
me12975
 
5.2%
virgin11653
 
4.6%
money11653
 
4.6%
permanent9628
 
3.8%
newcastle9628
 
3.8%
athena9242
 
3.7%
Other values (27)103361
41.2%

Most occurring characters

ValueCountFrequency (%)
a179651
 
10.8%
n158203
 
9.5%
e127793
 
7.7%
o92897
 
5.6%
s83861
 
5.0%
t82206
 
4.9%
73091
 
4.4%
B67610
 
4.1%
u67091
 
4.0%
i65974
 
4.0%
Other values (34)664454
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1184985
71.3%
Uppercase Letter346003
 
20.8%
Space Separator73091
 
4.4%
Other Punctuation58752
 
3.5%

Most frequent character per category

ValueCountFrequency (%)
a179651
15.2%
n158203
13.4%
e127793
10.8%
o92897
7.8%
s83861
 
7.1%
t82206
 
6.9%
u67091
 
5.7%
i65974
 
5.6%
r55390
 
4.7%
l53225
 
4.5%
Other values (11)218694
18.5%
ValueCountFrequency (%)
B67610
19.5%
A41074
11.9%
H35563
10.3%
M32212
9.3%
C23031
 
6.7%
U23026
 
6.7%
N22433
 
6.5%
S19277
 
5.6%
Q16767
 
4.8%
E13650
 
3.9%
Other values (10)51360
14.8%
ValueCountFrequency (%)
.55320
94.2%
:3432
 
5.8%
ValueCountFrequency (%)
73091
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1530988
92.1%
Common131843
 
7.9%

Most frequent character per script

ValueCountFrequency (%)
a179651
 
11.7%
n158203
 
10.3%
e127793
 
8.3%
o92897
 
6.1%
s83861
 
5.5%
t82206
 
5.4%
B67610
 
4.4%
u67091
 
4.4%
i65974
 
4.3%
r55390
 
3.6%
Other values (31)550312
35.9%
ValueCountFrequency (%)
73091
55.4%
.55320
42.0%
:3432
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1662831
100.0%

Most frequent character per block

ValueCountFrequency (%)
a179651
 
10.8%
n158203
 
9.5%
e127793
 
7.7%
o92897
 
5.6%
s83861
 
5.0%
t82206
 
4.9%
73091
 
4.4%
B67610
 
4.1%
u67091
 
4.0%
i65974
 
4.0%
Other values (34)664454
40.0%

rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.709884909
Minimum3.39
Maximum4.53
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2021-02-01T02:40:17.289482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.39
5-th percentile3.48
Q13.59
median3.68
Q33.86
95-th percentile3.99
Maximum4.53
Range1.14
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.1768662397
Coefficient of variation (CV)0.04767431984
Kurtosis-0.5207700426
Mean3.709884909
Median Absolute Deviation (MAD)0.09
Skewness0.6118866995
Sum658549.09
Variance0.03128166676
MonotocityNot monotonic
2021-02-01T02:40:17.456971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3.5936330
20.5%
3.9920590
11.6%
3.6918627
10.5%
3.4813097
 
7.4%
3.8912723
 
7.2%
3.6411354
 
6.4%
3.499065
 
5.1%
3.766886
 
3.9%
3.976768
 
3.8%
3.585714
 
3.2%
Other values (18)36358
20.5%
ValueCountFrequency (%)
3.39314
 
0.2%
3.441246
 
0.7%
3.472903
 
1.6%
3.4813097
7.4%
3.499065
5.1%
ValueCountFrequency (%)
4.531
 
< 0.1%
4.193180
 
1.8%
3.9920590
11.6%
3.976768
 
3.8%
3.8912723
7.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size173.5 KiB
False
138155 
True
39357 
ValueCountFrequency (%)
False138155
77.8%
True39357
 
22.2%
2021-02-01T02:40:17.544582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Region
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
New South Wales
65829 
Victoria
49816 
Queensland
29365 
Western Australia
15505 
South Australia
11123 
Other values (3)
 
5874

Length

Max length28
Median length15
Mean length12.62542814
Min length8

Characters and Unicode

Total characters2241165
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVictoria
2nd rowVictoria
3rd rowVictoria
4th rowVictoria
5th rowSouth Australia
ValueCountFrequency (%)
New South Wales65829
37.1%
Victoria49816
28.1%
Queensland29365
16.5%
Western Australia15505
 
8.7%
South Australia11123
 
6.3%
Australian Capital Territory4047
 
2.3%
Tasmania1508
 
0.8%
Northern Territory319
 
0.2%
2021-02-01T02:40:17.862392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-01T02:40:18.022253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
south76952
22.4%
wales65829
19.1%
new65829
19.1%
victoria49816
14.5%
queensland29365
 
8.5%
australia26628
 
7.7%
western15505
 
4.5%
territory4366
 
1.3%
capital4047
 
1.2%
australian4047
 
1.2%
Other values (2)1827
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e226083
 
10.1%
a218978
 
9.8%
t181680
 
8.1%
166699
 
7.4%
s142882
 
6.4%
i140228
 
6.3%
u136992
 
6.1%
o131453
 
5.9%
l129916
 
5.8%
r109732
 
4.9%
Other values (16)656522
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1730255
77.2%
Uppercase Letter344211
 
15.4%
Space Separator166699
 
7.4%

Most frequent character per category

ValueCountFrequency (%)
e226083
13.1%
a218978
12.7%
t181680
10.5%
s142882
8.3%
i140228
8.1%
u136992
7.9%
o131453
7.6%
l129916
7.5%
r109732
6.3%
n80109
 
4.6%
Other values (7)232202
13.4%
ValueCountFrequency (%)
W81334
23.6%
S76952
22.4%
N66148
19.2%
V49816
14.5%
A30675
 
8.9%
Q29365
 
8.5%
T5874
 
1.7%
C4047
 
1.2%
ValueCountFrequency (%)
166699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2074466
92.6%
Common166699
 
7.4%

Most frequent character per script

ValueCountFrequency (%)
e226083
 
10.9%
a218978
 
10.6%
t181680
 
8.8%
s142882
 
6.9%
i140228
 
6.8%
u136992
 
6.6%
o131453
 
6.3%
l129916
 
6.3%
r109732
 
5.3%
W81334
 
3.9%
Other values (15)575188
27.7%
ValueCountFrequency (%)
166699
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2241165
100.0%

Most frequent character per block

ValueCountFrequency (%)
e226083
 
10.1%
a218978
 
9.8%
t181680
 
8.1%
166699
 
7.4%
s142882
 
6.4%
i140228
 
6.3%
u136992
 
6.1%
o131453
 
5.9%
l129916
 
5.8%
r109732
 
4.9%
Other values (16)656522
29.3%

rate_rank
Real number (ℝ≥0)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.301821849
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2021-02-01T02:40:18.257890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q311
95-th percentile16
Maximum21
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.839384593
Coefficient of variation (CV)0.6627639914
Kurtosis-0.7357837919
Mean7.301821849
Median Absolute Deviation (MAD)4
Skewness0.5123256183
Sum1296161
Variance23.41964324
MonotocityNot monotonic
2021-02-01T02:40:18.690315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
118360
 
10.3%
216940
 
9.5%
315291
 
8.6%
413795
 
7.8%
512641
 
7.1%
611738
 
6.6%
711066
 
6.2%
810460
 
5.9%
99906
 
5.6%
109143
 
5.2%
Other values (11)48172
27.1%
ValueCountFrequency (%)
118360
10.3%
216940
9.5%
315291
8.6%
413795
7.8%
512641
7.1%
ValueCountFrequency (%)
21175
 
0.1%
20468
 
0.3%
191221
 
0.7%
182417
1.4%
173055
1.7%

min_interest
Real number (ℝ≥0)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.497545631
Minimum3.39
Maximum3.99
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2021-02-01T02:40:18.861323image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.39
5-th percentile3.44
Q13.47
median3.48
Q33.49
95-th percentile3.59
Maximum3.99
Range0.6
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.05288952664
Coefficient of variation (CV)0.01512189753
Kurtosis2.728578476
Mean3.497545631
Median Absolute Deviation (MAD)0.01
Skewness1.364241179
Sum620856.32
Variance0.002797302029
MonotocityNot monotonic
2021-02-01T02:40:19.000213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3.4877645
43.7%
3.4735501
20.0%
3.5925565
 
14.4%
3.4417813
 
10.0%
3.5811397
 
6.4%
3.495506
 
3.1%
3.392097
 
1.2%
3.641092
 
0.6%
3.68188
 
0.1%
3.54185
 
0.1%
Other values (12)523
 
0.3%
ValueCountFrequency (%)
3.392097
 
1.2%
3.4417813
 
10.0%
3.4735501
20.0%
3.4877645
43.7%
3.495506
 
3.1%
ValueCountFrequency (%)
3.9942
< 0.1%
3.892
 
< 0.1%
3.8441
< 0.1%
3.87
 
< 0.1%
3.7973
< 0.1%

TotalImpressions
Real number (ℝ≥0)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.6036437
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2021-02-01T02:40:19.201084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median15
Q318
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.923808488
Coefficient of variation (CV)0.3619477691
Kurtosis-0.2243364948
Mean13.6036437
Median Absolute Deviation (MAD)3
Skewness-0.8171735153
Sum2414810
Variance24.24389003
MonotocityNot monotonic
2021-02-01T02:40:19.356292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1523445
13.2%
1821528
12.1%
1417136
 
9.7%
1616224
 
9.1%
1914307
 
8.1%
1710846
 
6.1%
138892
 
5.0%
126924
 
3.9%
96867
 
3.9%
205860
 
3.3%
Other values (11)45483
25.6%
ValueCountFrequency (%)
11420
 
0.8%
23298
1.9%
34488
2.5%
44616
2.6%
54515
2.5%
ValueCountFrequency (%)
213675
 
2.1%
205860
 
3.3%
1914307
8.1%
1821528
12.1%
1710846
6.1%

rate_var
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2123392785
Minimum0
Maximum1.04
Zeros20961
Zeros (%)11.8%
Memory size1.4 MiB
2021-02-01T02:40:19.540583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.17
Q30.31
95-th percentile0.52
Maximum1.04
Range1.04
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.1753962678
Coefficient of variation (CV)0.82601895
Kurtosis-0.5398523727
Mean0.2123392785
Median Absolute Deviation (MAD)0.12
Skewness0.6714568202
Sum37692.77
Variance0.03076385077
MonotocityNot monotonic
2021-02-01T02:40:19.737814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020961
 
11.8%
0.1118579
 
10.5%
0.519112
 
5.1%
0.218501
 
4.8%
0.417627
 
4.3%
0.127331
 
4.1%
0.156426
 
3.6%
0.015813
 
3.3%
0.225760
 
3.2%
0.15445
 
3.1%
Other values (57)81957
46.2%
ValueCountFrequency (%)
020961
11.8%
0.015813
 
3.3%
0.014600
 
2.6%
0.022389
 
1.3%
0.0338
 
< 0.1%
ValueCountFrequency (%)
1.041
 
< 0.1%
0.75478
0.3%
0.71453
0.3%
0.7210
 
0.1%
0.61637
0.4%

LoanType
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Variable
160647 
Fixed
16865 

Length

Max length8
Median length8
Mean length7.714977016
Min length5

Characters and Unicode

Total characters1369501
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVariable
2nd rowVariable
3rd rowVariable
4th rowVariable
5th rowVariable
ValueCountFrequency (%)
Variable160647
90.5%
Fixed16865
 
9.5%
2021-02-01T02:40:20.059628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-01T02:40:20.175938image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
variable160647
90.5%
fixed16865
 
9.5%

Most occurring characters

ValueCountFrequency (%)
a321294
23.5%
i177512
13.0%
e177512
13.0%
V160647
11.7%
r160647
11.7%
b160647
11.7%
l160647
11.7%
F16865
 
1.2%
x16865
 
1.2%
d16865
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1191989
87.0%
Uppercase Letter177512
 
13.0%

Most frequent character per category

ValueCountFrequency (%)
a321294
27.0%
i177512
14.9%
e177512
14.9%
r160647
13.5%
b160647
13.5%
l160647
13.5%
x16865
 
1.4%
d16865
 
1.4%
ValueCountFrequency (%)
V160647
90.5%
F16865
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1369501
100.0%

Most frequent character per script

ValueCountFrequency (%)
a321294
23.5%
i177512
13.0%
e177512
13.0%
V160647
11.7%
r160647
11.7%
b160647
11.7%
l160647
11.7%
F16865
 
1.2%
x16865
 
1.2%
d16865
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1369501
100.0%

Most frequent character per block

ValueCountFrequency (%)
a321294
23.5%
i177512
13.0%
e177512
13.0%
V160647
11.7%
r160647
11.7%
b160647
11.7%
l160647
11.7%
F16865
 
1.2%
x16865
 
1.2%
d16865
 
1.2%

Big4Weight
Real number (ℝ≥0)

ZEROS

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03571026184
Minimum0
Maximum1
Zeros83856
Zeros (%)47.2%
Memory size1.4 MiB
2021-02-01T02:40:20.298980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.05
Q30.06666666667
95-th percentile0.08333333333
Maximum1
Range1
Interquartile range (IQR)0.06666666667

Descriptive statistics

Standard deviation0.03813390568
Coefficient of variation (CV)1.067869674
Kurtosis11.71007257
Mean0.03571026184
Median Absolute Deviation (MAD)0.05
Skewness1.452749277
Sum6339
Variance0.001454194762
MonotocityNot monotonic
2021-02-01T02:40:20.470359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
083856
47.2%
0.0666666666717820
 
10.0%
0.0714285714313300
 
7.5%
0.0526315789510868
 
6.1%
0.0555555555610134
 
5.7%
0.062510000
 
5.6%
0.055820
 
3.3%
0.058823529415695
 
3.2%
0.076923076924654
 
2.6%
0.047619047623675
 
2.1%
Other values (13)11690
 
6.6%
ValueCountFrequency (%)
083856
47.2%
0.047619047623675
 
2.1%
0.055820
 
3.3%
0.0526315789510868
 
6.1%
0.0555555555610134
 
5.7%
ValueCountFrequency (%)
12
 
< 0.1%
0.532
 
< 0.1%
0.3333333333114
 
0.1%
0.25300
0.2%
0.2365
0.2%

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
174056 
1
 
3456

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0174056
98.1%
13456
 
1.9%
2021-02-01T02:40:20.790483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-01T02:40:20.892607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0174056
98.1%
13456
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0174056
98.1%
13456
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number177512
100.0%

Most frequent character per category

ValueCountFrequency (%)
0174056
98.1%
13456
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common177512
100.0%

Most frequent character per script

ValueCountFrequency (%)
0174056
98.1%
13456
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII177512
100.0%

Most frequent character per block

ValueCountFrequency (%)
0174056
98.1%
13456
 
1.9%

Browser_OS
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Chrome_Windows
50446 
Safari_iOS
48465 
Chrome_Android
26966 
Edge_Windows
12515 
Chrome_Macintosh
11074 
Other values (11)
28046 

Length

Max length23
Median length14
Mean length13.26903533
Min length10

Characters and Unicode

Total characters2355413
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSafari_iOS
2nd rowSafari_iOS
3rd rowSafari_iOS
4th rowSafari_iOS
5th rowChrome_Android
ValueCountFrequency (%)
Chrome_Windows50446
28.4%
Safari_iOS48465
27.3%
Chrome_Android26966
15.2%
Edge_Windows12515
 
7.1%
Chrome_Macintosh11074
 
6.2%
SamsungInternet_Android7590
 
4.3%
Safari_Macintosh6489
 
3.7%
Others_Windows5425
 
3.1%
Chrome_iOS4082
 
2.3%
Others_Android2648
 
1.5%
Other values (6)1812
 
1.0%
2021-02-01T02:40:21.182115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome_windows50446
28.4%
safari_ios48465
27.3%
chrome_android26966
15.2%
edge_windows12515
 
7.1%
chrome_macintosh11074
 
6.2%
samsunginternet_android7590
 
4.3%
safari_macintosh6489
 
3.7%
others_windows5425
 
3.1%
chrome_ios4082
 
2.3%
others_android2648
 
1.5%
Other values (6)1812
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i231940
 
9.8%
o217422
 
9.2%
r202725
 
8.6%
_177512
 
7.5%
d155311
 
6.6%
n147211
 
6.2%
a136343
 
5.8%
e130677
 
5.5%
h121816
 
5.2%
S115101
 
4.9%
Other values (15)719355
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1762730
74.8%
Uppercase Letter415171
 
17.6%
Connector Punctuation177512
 
7.5%

Most frequent character per category

ValueCountFrequency (%)
i231940
13.2%
o217422
12.3%
r202725
11.5%
d155311
8.8%
n147211
8.4%
a136343
7.7%
e130677
7.4%
h121816
6.9%
s104805
5.9%
m100583
5.7%
Other values (6)213897
12.1%
ValueCountFrequency (%)
S115101
27.7%
C92990
22.4%
W68386
16.5%
O62538
15.1%
A37204
 
9.0%
M18842
 
4.5%
E12517
 
3.0%
I7593
 
1.8%
ValueCountFrequency (%)
_177512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2177901
92.5%
Common177512
 
7.5%

Most frequent character per script

ValueCountFrequency (%)
i231940
 
10.6%
o217422
 
10.0%
r202725
 
9.3%
d155311
 
7.1%
n147211
 
6.8%
a136343
 
6.3%
e130677
 
6.0%
h121816
 
5.6%
S115101
 
5.3%
s104805
 
4.8%
Other values (14)614550
28.2%
ValueCountFrequency (%)
_177512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2355413
100.0%

Most frequent character per block

ValueCountFrequency (%)
i231940
 
9.8%
o217422
 
9.2%
r202725
 
8.6%
_177512
 
7.5%
d155311
 
6.6%
n147211
 
6.2%
a136343
 
5.8%
e130677
 
5.5%
h121816
 
5.2%
S115101
 
4.9%
Other values (15)719355
30.5%

Interactions

2021-02-01T02:39:50.366565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:50.662154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:50.874981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:51.126175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:51.338177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:51.532886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:51.770347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:51.990127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:52.188389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:52.410204image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:52.620687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:52.850224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:53.099264image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:53.358454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:53.564509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:53.800605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:53.957914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:54.124322image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:54.284170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:54.451141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:54.660165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:54.833850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:55.030187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:55.232065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:55.461248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:55.692203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:55.901870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:56.120009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:56.355003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:56.557709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:56.764051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:57.671036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:57.885879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:58.151049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:58.403943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:58.635871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:58.880009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:59.142107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:59.364681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:59.649896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:39:59.932820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:00.136954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:00.321122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:00.535523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:00.746377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:00.964473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:01.270780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:01.499157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:01.678529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:01.909300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:02.105017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:02.297008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:02.493358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:02.676275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:02.870983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:03.084852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:03.302817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:03.513786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:03.710884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:03.920316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:04.130924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:04.321573image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:04.524196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:04.702980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:04.856652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:05.018364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:05.325846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:05.489808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:05.645082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:05.813861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:05.973240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-01T02:40:06.158646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-02-01T02:40:21.347536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-01T02:40:21.588398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-01T02:40:21.852918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-01T02:40:22.162927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-01T02:40:22.493476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-01T02:40:06.752357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-01T02:40:07.736997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0click_typeTime_of_dayweekdaypositiondeviceprovider_namerateexperts_choiceRegionrate_rankmin_interestTotalImpressionsrate_varLoanTypeBig4WeighttargetBrowser_OS
01GTS3Friday4mobileNewcastle Permanent3.89TrueVictoria23.7440.15Variable0.250Safari_iOS
12GTS3Friday5mobileME3.89FalseVictoria33.7440.15Variable0.250Safari_iOS
23GTS3Friday6mobileNAB3.99FalseVictoria43.7440.25Variable0.250Safari_iOS
34GTS3Friday7mobileAMP3.74FalseVictoria13.7440.00Variable0.250Safari_iOS
45GTS6Friday1mobileloans.com.au3.64TrueSouth Australia33.5970.05Variable0.000Chrome_Android
56GTS6Friday2mobileUBank3.59FalseSouth Australia13.5970.00Variable0.000Chrome_Android
67GTS6Friday3mobileHSBC3.59FalseSouth Australia23.5970.00Variable0.000Chrome_Android
78GTS6Friday9mobileHunter United4.19FalseSouth Australia73.5970.60Variable0.000Chrome_Android
89GTS6Friday8mobileGateway Bank3.64FalseSouth Australia43.5970.05Variable0.000Chrome_Android
910GTS6Friday12mobileMacquarie3.80FalseSouth Australia53.5970.21Variable0.000Chrome_Android

Last rows

Unnamed: 0click_typeTime_of_dayweekdaypositiondeviceprovider_namerateexperts_choiceRegionrate_rankmin_interestTotalImpressionsrate_varLoanTypeBig4WeighttargetBrowser_OS
177502177503GTS23Friday7desktopNewcastle Permanent3.89TrueVictoria133.39160.50Variable0.00Safari_Macintosh
177503177504GTS23Friday8desktopHomestar3.49FalseVictoria63.39160.10Variable0.00Safari_Macintosh
177504177505GTS23Friday10desktopTic:Toc3.47TrueVictoria33.39160.08Variable0.00Safari_Macintosh
177505177506GTS23Friday11desktoploans.com.au3.48FalseVictoria53.39160.09Fixed0.00Safari_Macintosh
177506177507GTS23Friday12desktopHunter United3.99FalseVictoria143.39160.60Variable0.00Safari_Macintosh
177507177508Lead23Friday13desktopVirgin Money3.69FalseVictoria123.39160.30Variable0.00Safari_Macintosh
177508177509GTS23Friday14desktoploans.com.au3.64TrueVictoria103.39160.25Variable0.00Safari_Macintosh
177509177510Lead23Friday15desktopBank of Queensland3.99FalseVictoria153.39160.60Variable0.00Safari_Macintosh
177510177511Lead23Friday16desktopAussie3.99FalseVictoria163.39160.60Variable0.00Safari_Macintosh
177511177512GTS23Friday18desktopMOVE Bank3.59TrueVictoria93.39160.20Variable0.00Safari_Macintosh